Dispelling Potential Fears Associated with Stem Cell Donation
Bibliographic record
Abstract
An increasing number of patients require life-saving stem cell transplants, often from unrelated donors. In order to facilitate this process, bone marrow and stem cell registries have been established to genetically catalog potential donors and can be used to find matches for patients in need. Given the wide genetic variability in populations and significant ethnic disparities in donor registries worldwide, there are substantial gaps in the availability of compatible unrelated stem cell donors. Limited understanding of the procedures involved in stem cell donation, along with potential misconceptions of associated risks, may discourage prospective donors. Many people are unaware that there are two established methods for stem cell donation from adult donors, either through bone marrow harvest or—more commonly— through peripheral blood stem cell harvest. This evidence-based commentary explores these two procedures, deconstructs misconstrued fears associated with stem cell donation, and subsequently encourages readers to consider registering as stem cell donors.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.049 | 0.157 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.014 | 0.059 |
| Scholarly communication | 0.012 | 0.013 |
| Open science | 0.005 | 0.010 |
| Research integrity | 0.047 | 0.049 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".